Overview

Dataset statistics

Number of variables156
Number of observations509599
Missing cells65876715
Missing cells (%)82.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory729.9 MiB
Average record size in memory1.5 KiB

Variable types

Text2
Boolean3
Categorical151

Alerts

is_current_customer is highly imbalanced (87.3%)Imbalance
crossbeam_product1_customer is highly imbalanced (98.6%)Imbalance
crossbeam_product2_customer is highly imbalanced (98.7%)Imbalance
crossbeam_product3_customer is highly imbalanced (98.3%)Imbalance
crossbeam_product4_customer is highly imbalanced (99.5%)Imbalance
crossbeam_product5_customer is highly imbalanced (95.9%)Imbalance
crossbeam_product6_customer is highly imbalanced (74.9%)Imbalance
crossbeam_product7_customer is highly imbalanced (96.4%)Imbalance
crossbeam_product8_customer is highly imbalanced (95.2%)Imbalance
crossbeam_product9_customer is highly imbalanced (98.8%)Imbalance
crossbeam_product10_customer is highly imbalanced (92.9%)Imbalance
crossbeam_product11_customer is highly imbalanced (96.9%)Imbalance
crossbeam_product12_customer is highly imbalanced (95.0%)Imbalance
crossbeam_product13_customer is highly imbalanced (99.2%)Imbalance
crossbeam_product14_customer is highly imbalanced (94.0%)Imbalance
crossbeam_product15_customer is highly imbalanced (81.5%)Imbalance
crossbeam_product16_customer is highly imbalanced (98.3%)Imbalance
crossbeam_product17_customer is highly imbalanced (98.5%)Imbalance
crossbeam_product18_customer is highly imbalanced (96.2%)Imbalance
crossbeam_product19_customer is highly imbalanced (92.6%)Imbalance
crossbeam_product20_customer is highly imbalanced (68.5%)Imbalance
crossbeam_product21_customer is highly imbalanced (94.9%)Imbalance
crossbeam_product22_customer is highly imbalanced (97.3%)Imbalance
has_hg_data is highly imbalanced (55.0%)Imbalance
hg_product_27 is highly imbalanced (> 99.9%)Imbalance
hg_product_28 is highly imbalanced (62.0%)Imbalance
hg_product_29 is highly imbalanced (87.5%)Imbalance
hg_product_30 is highly imbalanced (89.1%)Imbalance
hg_product_35 is highly imbalanced (94.0%)Imbalance
hg_product_36 is highly imbalanced (56.0%)Imbalance
hg_product_37 is highly imbalanced (68.4%)Imbalance
hg_product_39 is highly imbalanced (52.6%)Imbalance
hg_product_40 is highly imbalanced (96.5%)Imbalance
hg_product_41 is highly imbalanced (60.8%)Imbalance
hg_product_42 is highly imbalanced (60.8%)Imbalance
hg_product_43 is highly imbalanced (89.0%)Imbalance
hg_product_44 is highly imbalanced (55.7%)Imbalance
hg_product_46 is highly imbalanced (97.9%)Imbalance
hg_product_49 is highly imbalanced (78.3%)Imbalance
hg_product_51 is highly imbalanced (73.1%)Imbalance
hg_product_52 is highly imbalanced (80.5%)Imbalance
hg_product_53 is highly imbalanced (88.0%)Imbalance
hg_product_54 is highly imbalanced (98.9%)Imbalance
hg_product_56 is highly imbalanced (98.4%)Imbalance
hg_product_57 is highly imbalanced (63.3%)Imbalance
hg_product_58 is highly imbalanced (99.4%)Imbalance
hg_product_59 is highly imbalanced (99.7%)Imbalance
hg_product_60 is highly imbalanced (95.9%)Imbalance
hg_product_61 is highly imbalanced (99.4%)Imbalance
hg_product_62 is highly imbalanced (68.1%)Imbalance
hg_product_64 is highly imbalanced (97.3%)Imbalance
hg_product_65 is highly imbalanced (69.8%)Imbalance
hg_product_66 is highly imbalanced (90.5%)Imbalance
hg_product_68 is highly imbalanced (56.8%)Imbalance
hg_product_70 is highly imbalanced (81.8%)Imbalance
hg_product_71 is highly imbalanced (91.3%)Imbalance
hg_product_72 is highly imbalanced (65.8%)Imbalance
hg_product_74 is highly imbalanced (73.5%)Imbalance
hg_product_75 is highly imbalanced (63.7%)Imbalance
hg_product_76 is highly imbalanced (50.5%)Imbalance
hg_product_78 is highly imbalanced (77.6%)Imbalance
hg_product_79 is highly imbalanced (62.1%)Imbalance
hg_product_80 is highly imbalanced (79.2%)Imbalance
hg_product_81 is highly imbalanced (67.2%)Imbalance
hg_product_82 is highly imbalanced (99.8%)Imbalance
hg_product_84 is highly imbalanced (88.9%)Imbalance
hg_product_85 is highly imbalanced (99.7%)Imbalance
hg_product_86 is highly imbalanced (89.6%)Imbalance
hg_product_88 is highly imbalanced (55.8%)Imbalance
hg_product_89 is highly imbalanced (85.6%)Imbalance
hg_product_90 is highly imbalanced (82.3%)Imbalance
hg_product_91 is highly imbalanced (99.6%)Imbalance
hg_product_92 is highly imbalanced (92.5%)Imbalance
hg_product_93 is highly imbalanced (99.9%)Imbalance
hg_product_94 is highly imbalanced (98.1%)Imbalance
hg_product_95 is highly imbalanced (76.7%)Imbalance
hg_product_96 is highly imbalanced (62.3%)Imbalance
hg_product_97 is highly imbalanced (95.1%)Imbalance
hg_product_98 is highly imbalanced (91.8%)Imbalance
hg_product_99 is highly imbalanced (92.3%)Imbalance
hg_product_100 is highly imbalanced (83.2%)Imbalance
hg_product_101 is highly imbalanced (86.7%)Imbalance
hg_product_103 is highly imbalanced (57.7%)Imbalance
hg_product_104 is highly imbalanced (90.3%)Imbalance
hg_product_105 is highly imbalanced (68.0%)Imbalance
hg_product_106 is highly imbalanced (96.3%)Imbalance
hg_product_107 is highly imbalanced (95.4%)Imbalance
hg_product_108 is highly imbalanced (99.9%)Imbalance
hg_product_109 is highly imbalanced (98.8%)Imbalance
hg_product_110 is highly imbalanced (97.2%)Imbalance
hg_product_111 is highly imbalanced (99.4%)Imbalance
hg_product_112 is highly imbalanced (73.9%)Imbalance
hg_product_113 is highly imbalanced (99.6%)Imbalance
hg_product_114 is highly imbalanced (75.6%)Imbalance
hg_product_115 is highly imbalanced (98.4%)Imbalance
hg_product_116 is highly imbalanced (92.2%)Imbalance
hg_product_117 is highly imbalanced (97.1%)Imbalance
hg_product_118 is highly imbalanced (99.1%)Imbalance
hg_product_119 is highly imbalanced (66.8%)Imbalance
hg_product_120 is highly imbalanced (92.6%)Imbalance
hg_product_121 is highly imbalanced (98.4%)Imbalance
hg_product_122 is highly imbalanced (99.7%)Imbalance
hg_product_123 is highly imbalanced (85.7%)Imbalance
hg_product_126 is highly imbalanced (89.8%)Imbalance
hg_product_127 is highly imbalanced (91.6%)Imbalance
hg_product_128 is highly imbalanced (99.3%)Imbalance
hg_product_130 is highly imbalanced (97.0%)Imbalance
hg_product_131 is highly imbalanced (95.7%)Imbalance
hg_product_132 is highly imbalanced (50.3%)Imbalance
hg_product_133 is highly imbalanced (96.3%)Imbalance
hg_product_134 is highly imbalanced (97.4%)Imbalance
hg_product_135 is highly imbalanced (99.6%)Imbalance
hg_product_136 is highly imbalanced (99.4%)Imbalance
hg_product_137 is highly imbalanced (92.2%)Imbalance
hg_product_138 is highly imbalanced (95.4%)Imbalance
hg_product_139 is highly imbalanced (60.4%)Imbalance
hg_product_141 is highly imbalanced (86.3%)Imbalance
hg_product_142 is highly imbalanced (92.0%)Imbalance
hg_product_143 is highly imbalanced (56.5%)Imbalance
hg_product_146 is highly imbalanced (96.0%)Imbalance
hg_product_147 is highly imbalanced (93.5%)Imbalance
hg_product_148 is highly imbalanced (98.9%)Imbalance
is_self_service has 500720 (98.3%) missing valuesMissing
is_arr_over_12k has 500720 (98.3%) missing valuesMissing
company_revenue_bucket has 467922 (91.8%) missing valuesMissing
country has 39738 (7.8%) missing valuesMissing
state has 319941 (62.8%) missing valuesMissing
industry_grouped has 69660 (13.7%) missing valuesMissing
crossbeam_product1_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product2_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product3_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product4_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product5_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product6_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product7_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product8_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product9_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product10_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product11_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product12_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product13_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product14_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product15_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product16_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product17_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product18_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product19_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product20_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product21_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product22_customer has 326470 (64.1%) missing valuesMissing
crossbeam_product23_customer has 326470 (64.1%) missing valuesMissing
dnb_founded_time_grouped has 301502 (59.2%) missing valuesMissing
hg_product_27 has 460391 (90.3%) missing valuesMissing
hg_product_28 has 460391 (90.3%) missing valuesMissing
hg_product_29 has 460391 (90.3%) missing valuesMissing
hg_product_30 has 460391 (90.3%) missing valuesMissing
hg_product_31 has 460391 (90.3%) missing valuesMissing
hg_product_32 has 460391 (90.3%) missing valuesMissing
hg_product_33 has 460391 (90.3%) missing valuesMissing
hg_product_34 has 460391 (90.3%) missing valuesMissing
hg_product_35 has 460391 (90.3%) missing valuesMissing
hg_product_36 has 460391 (90.3%) missing valuesMissing
hg_product_37 has 460391 (90.3%) missing valuesMissing
hg_product_38 has 460391 (90.3%) missing valuesMissing
hg_product_39 has 460391 (90.3%) missing valuesMissing
hg_product_40 has 460391 (90.3%) missing valuesMissing
hg_product_41 has 460391 (90.3%) missing valuesMissing
hg_product_42 has 460391 (90.3%) missing valuesMissing
hg_product_43 has 460391 (90.3%) missing valuesMissing
hg_product_44 has 460391 (90.3%) missing valuesMissing
hg_product_45 has 460391 (90.3%) missing valuesMissing
hg_product_46 has 460391 (90.3%) missing valuesMissing
hg_product_47 has 460391 (90.3%) missing valuesMissing
hg_product_48 has 460391 (90.3%) missing valuesMissing
hg_product_49 has 460391 (90.3%) missing valuesMissing
hg_product_50 has 460391 (90.3%) missing valuesMissing
hg_product_51 has 460391 (90.3%) missing valuesMissing
hg_product_52 has 460391 (90.3%) missing valuesMissing
hg_product_53 has 460391 (90.3%) missing valuesMissing
hg_product_54 has 460391 (90.3%) missing valuesMissing
hg_product_55 has 460391 (90.3%) missing valuesMissing
hg_product_56 has 460391 (90.3%) missing valuesMissing
hg_product_57 has 460391 (90.3%) missing valuesMissing
hg_product_58 has 460391 (90.3%) missing valuesMissing
hg_product_59 has 460391 (90.3%) missing valuesMissing
hg_product_60 has 460391 (90.3%) missing valuesMissing
hg_product_61 has 460391 (90.3%) missing valuesMissing
hg_product_62 has 460391 (90.3%) missing valuesMissing
hg_product_63 has 460391 (90.3%) missing valuesMissing
hg_product_64 has 460391 (90.3%) missing valuesMissing
hg_product_65 has 460391 (90.3%) missing valuesMissing
hg_product_66 has 460391 (90.3%) missing valuesMissing
hg_product_67 has 460391 (90.3%) missing valuesMissing
hg_product_68 has 460391 (90.3%) missing valuesMissing
hg_product_69 has 460391 (90.3%) missing valuesMissing
hg_product_70 has 460391 (90.3%) missing valuesMissing
hg_product_71 has 460391 (90.3%) missing valuesMissing
hg_product_72 has 460391 (90.3%) missing valuesMissing
hg_product_73 has 460391 (90.3%) missing valuesMissing
hg_product_74 has 460391 (90.3%) missing valuesMissing
hg_product_75 has 460391 (90.3%) missing valuesMissing
hg_product_76 has 460391 (90.3%) missing valuesMissing
hg_product_77 has 460391 (90.3%) missing valuesMissing
hg_product_78 has 460391 (90.3%) missing valuesMissing
hg_product_79 has 460391 (90.3%) missing valuesMissing
hg_product_80 has 460391 (90.3%) missing valuesMissing
hg_product_81 has 460391 (90.3%) missing valuesMissing
hg_product_82 has 460391 (90.3%) missing valuesMissing
hg_product_83 has 460391 (90.3%) missing valuesMissing
hg_product_84 has 460391 (90.3%) missing valuesMissing
hg_product_85 has 460391 (90.3%) missing valuesMissing
hg_product_86 has 460391 (90.3%) missing valuesMissing
hg_product_87 has 460391 (90.3%) missing valuesMissing
hg_product_88 has 460391 (90.3%) missing valuesMissing
hg_product_89 has 460391 (90.3%) missing valuesMissing
hg_product_90 has 460391 (90.3%) missing valuesMissing
hg_product_91 has 460391 (90.3%) missing valuesMissing
hg_product_92 has 460391 (90.3%) missing valuesMissing
hg_product_93 has 460391 (90.3%) missing valuesMissing
hg_product_94 has 460391 (90.3%) missing valuesMissing
hg_product_95 has 460391 (90.3%) missing valuesMissing
hg_product_96 has 460391 (90.3%) missing valuesMissing
hg_product_97 has 460391 (90.3%) missing valuesMissing
hg_product_98 has 460391 (90.3%) missing valuesMissing
hg_product_99 has 460391 (90.3%) missing valuesMissing
hg_product_100 has 460391 (90.3%) missing valuesMissing
hg_product_101 has 460391 (90.3%) missing valuesMissing
hg_product_102 has 460391 (90.3%) missing valuesMissing
hg_product_103 has 460391 (90.3%) missing valuesMissing
hg_product_104 has 460391 (90.3%) missing valuesMissing
hg_product_105 has 460391 (90.3%) missing valuesMissing
hg_product_106 has 460391 (90.3%) missing valuesMissing
hg_product_107 has 460391 (90.3%) missing valuesMissing
hg_product_108 has 460391 (90.3%) missing valuesMissing
hg_product_109 has 460391 (90.3%) missing valuesMissing
hg_product_110 has 460391 (90.3%) missing valuesMissing
hg_product_111 has 460391 (90.3%) missing valuesMissing
hg_product_112 has 460391 (90.3%) missing valuesMissing
hg_product_113 has 460391 (90.3%) missing valuesMissing
hg_product_114 has 460391 (90.3%) missing valuesMissing
hg_product_115 has 460391 (90.3%) missing valuesMissing
hg_product_116 has 460391 (90.3%) missing valuesMissing
hg_product_117 has 460391 (90.3%) missing valuesMissing
hg_product_118 has 460391 (90.3%) missing valuesMissing
hg_product_119 has 460391 (90.3%) missing valuesMissing
hg_product_120 has 460391 (90.3%) missing valuesMissing
hg_product_121 has 460391 (90.3%) missing valuesMissing
hg_product_122 has 460391 (90.3%) missing valuesMissing
hg_product_123 has 460391 (90.3%) missing valuesMissing
hg_product_124 has 460391 (90.3%) missing valuesMissing
hg_product_125 has 460391 (90.3%) missing valuesMissing
hg_product_126 has 460391 (90.3%) missing valuesMissing
hg_product_127 has 460391 (90.3%) missing valuesMissing
hg_product_128 has 460391 (90.3%) missing valuesMissing
hg_product_129 has 460391 (90.3%) missing valuesMissing
hg_product_130 has 460391 (90.3%) missing valuesMissing
hg_product_131 has 460391 (90.3%) missing valuesMissing
hg_product_132 has 460391 (90.3%) missing valuesMissing
hg_product_133 has 460391 (90.3%) missing valuesMissing
hg_product_134 has 460391 (90.3%) missing valuesMissing
hg_product_135 has 460391 (90.3%) missing valuesMissing
hg_product_136 has 460391 (90.3%) missing valuesMissing
hg_product_137 has 460391 (90.3%) missing valuesMissing
hg_product_138 has 460391 (90.3%) missing valuesMissing
hg_product_139 has 460391 (90.3%) missing valuesMissing
hg_product_140 has 460391 (90.3%) missing valuesMissing
hg_product_141 has 460391 (90.3%) missing valuesMissing
hg_product_142 has 460391 (90.3%) missing valuesMissing
hg_product_143 has 460391 (90.3%) missing valuesMissing
hg_product_144 has 460391 (90.3%) missing valuesMissing
hg_product_145 has 460391 (90.3%) missing valuesMissing
hg_product_146 has 460391 (90.3%) missing valuesMissing
hg_product_147 has 460391 (90.3%) missing valuesMissing
hg_product_148 has 460391 (90.3%) missing valuesMissing
account_id has unique valuesUnique

Reproduction

Analysis started2023-12-29 05:50:13.536178
Analysis finished2023-12-29 05:53:43.066087
Duration3 minutes and 29.53 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

account_id
Text

UNIQUE 

Distinct509599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size36.4 MiB
2023-12-28T21:53:43.670455image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters9172782
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509599 ?
Unique (%)100.0%

Sample

1st row001PY000002SuJDYA0
2nd row0011G00000nEkzYQAS
3rd row0011G00000qFM5EQAW
4th row0011G00000tKtSbQAK
5th row0011G00000tLivvQAC
ValueCountFrequency (%)
001py000002sujdya0 1
 
< 0.1%
0011g00000kvcljqae 1
 
< 0.1%
0011g00000tlivvqac 1
 
< 0.1%
0011g00000uogzkqa2 1
 
< 0.1%
0011g00000ufx52qaa 1
 
< 0.1%
0011g00000xa4ekqa0 1
 
< 0.1%
0011g0000120dn1qai 1
 
< 0.1%
0011g0000135y3dqaa 1
 
< 0.1%
001ho000013liqniau 1
 
< 0.1%
001ho000015sspjiac 1
 
< 0.1%
Other values (509589) 509589
> 99.9%
2023-12-28T21:53:44.653895image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3533017
38.5%
1 1074416
 
11.7%
A 611024
 
6.7%
Q 491331
 
5.4%
G 472809
 
5.2%
Y 137982
 
1.5%
I 130298
 
1.4%
w 102497
 
1.1%
o 100477
 
1.1%
n 88058
 
1.0%
Other values (52) 2430873
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000518
54.5%
Uppercase Letter 2846060
31.0%
Lowercase Letter 1326204
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 611024
21.5%
Q 491331
17.3%
G 472809
16.6%
Y 137982
 
4.8%
I 130298
 
4.6%
H 86188
 
3.0%
U 85973
 
3.0%
C 78209
 
2.7%
E 76664
 
2.7%
K 68702
 
2.4%
Other values (16) 606880
21.3%
Lowercase Letter
ValueCountFrequency (%)
w 102497
 
7.7%
o 100477
 
7.6%
n 88058
 
6.6%
h 76780
 
5.8%
k 75634
 
5.7%
q 65966
 
5.0%
u 60877
 
4.6%
j 60814
 
4.6%
g 56766
 
4.3%
y 48260
 
3.6%
Other values (16) 590075
44.5%
Decimal Number
ValueCountFrequency (%)
0 3533017
70.7%
1 1074416
 
21.5%
2 78484
 
1.6%
3 60244
 
1.2%
7 49855
 
1.0%
4 44146
 
0.9%
5 43696
 
0.9%
9 41396
 
0.8%
6 38676
 
0.8%
8 36588
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5000518
54.5%
Latin 4172264
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 611024
 
14.6%
Q 491331
 
11.8%
G 472809
 
11.3%
Y 137982
 
3.3%
I 130298
 
3.1%
w 102497
 
2.5%
o 100477
 
2.4%
n 88058
 
2.1%
H 86188
 
2.1%
U 85973
 
2.1%
Other values (42) 1865627
44.7%
Common
ValueCountFrequency (%)
0 3533017
70.7%
1 1074416
 
21.5%
2 78484
 
1.6%
3 60244
 
1.2%
7 49855
 
1.0%
4 44146
 
0.9%
5 43696
 
0.9%
9 41396
 
0.8%
6 38676
 
0.8%
8 36588
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9172782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3533017
38.5%
1 1074416
 
11.7%
A 611024
 
6.7%
Q 491331
 
5.4%
G 472809
 
5.2%
Y 137982
 
1.5%
I 130298
 
1.4%
w 102497
 
1.1%
o 100477
 
1.1%
n 88058
 
1.0%
Other values (52) 2430873
26.5%

is_current_customer
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
500720 
True
 
8879
ValueCountFrequency (%)
False 500720
98.3%
True 8879
 
1.7%
2023-12-28T21:53:44.918447image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

is_self_service
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing500720
Missing (%)98.3%
Memory size31.1 MiB
1.0
5843 
0.0
3036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5843
 
1.1%
0.0 3036
 
0.6%
(Missing) 500720
98.3%

Length

2023-12-28T21:53:45.090767image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:45.225438image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5843
65.8%
0.0 3036
34.2%

Most occurring characters

ValueCountFrequency (%)
0 11915
44.7%
. 8879
33.3%
1 5843
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
66.7%
Other Punctuation 8879
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11915
67.1%
1 5843
32.9%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11915
44.7%
. 8879
33.3%
1 5843
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11915
44.7%
. 8879
33.3%
1 5843
21.9%

is_arr_over_12k
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing500720
Missing (%)98.3%
Memory size31.1 MiB
0.0
5462 
1.0
3417 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5462
 
1.1%
1.0 3417
 
0.7%
(Missing) 500720
98.3%

Length

2023-12-28T21:53:45.370296image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:45.507932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5462
61.5%
1.0 3417
38.5%

Most occurring characters

ValueCountFrequency (%)
0 14341
53.8%
. 8879
33.3%
1 3417
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17758
66.7%
Other Punctuation 8879
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14341
80.8%
1 3417
 
19.2%
Other Punctuation
ValueCountFrequency (%)
. 8879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14341
53.8%
. 8879
33.3%
1 3417
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14341
53.8%
. 8879
33.3%
1 3417
 
12.8%

company_revenue_bucket
Categorical

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing467922
Missing (%)91.8%
Memory size31.2 MiB
Under 1B
18939 
Under 100M
9412 
Under 10B
8303 
Over 10B
2533 
Under 10M
 
1713

Length

Max length10
Median length8
Mean length8.6919884
Min length8

Characters and Unicode

Total characters362256
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnder 10B
2nd rowOver 10B
3rd rowUnder 1B
4th rowOver 10B
5th rowUnder 10B

Common Values

ValueCountFrequency (%)
Under 1B 18939
 
3.7%
Under 100M 9412
 
1.8%
Under 10B 8303
 
1.6%
Over 10B 2533
 
0.5%
Under 10M 1713
 
0.3%
Under 1M 777
 
0.2%
(Missing) 467922
91.8%

Length

2023-12-28T21:53:45.672616image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:45.835620image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
under 39144
47.0%
1b 18939
22.7%
10b 10836
 
13.0%
100m 9412
 
11.3%
over 2533
 
3.0%
10m 1713
 
2.1%
1m 777
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 41677
11.5%
r 41677
11.5%
41677
11.5%
1 41677
11.5%
U 39144
10.8%
n 39144
10.8%
d 39144
10.8%
0 31373
8.7%
B 29775
8.2%
M 11902
 
3.3%
Other values (2) 5066
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164175
45.3%
Uppercase Letter 83354
23.0%
Decimal Number 73050
20.2%
Space Separator 41677
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41677
25.4%
r 41677
25.4%
n 39144
23.8%
d 39144
23.8%
v 2533
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
U 39144
47.0%
B 29775
35.7%
M 11902
 
14.3%
O 2533
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 41677
57.1%
0 31373
42.9%
Space Separator
ValueCountFrequency (%)
41677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 247529
68.3%
Common 114727
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41677
16.8%
r 41677
16.8%
U 39144
15.8%
n 39144
15.8%
d 39144
15.8%
B 29775
12.0%
M 11902
 
4.8%
O 2533
 
1.0%
v 2533
 
1.0%
Common
ValueCountFrequency (%)
41677
36.3%
1 41677
36.3%
0 31373
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41677
11.5%
r 41677
11.5%
41677
11.5%
1 41677
11.5%
U 39144
10.8%
n 39144
10.8%
d 39144
10.8%
0 31373
8.7%
B 29775
8.2%
M 11902
 
3.3%
Other values (2) 5066
 
1.4%

country
Text

MISSING 

Distinct255
Distinct (%)0.1%
Missing39738
Missing (%)7.8%
Memory size27.7 MiB
2023-12-28T21:53:46.199900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length14
Median length2
Mean length2.0003278
Min length2

Characters and Unicode

Total characters939876
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)< 0.1%

Sample

1st rowCH
2nd rowNL
3rd rowRO
4th rowIN
5th rowUS
ValueCountFrequency (%)
us 192675
41.0%
gb 44376
 
9.4%
in 32745
 
7.0%
de 20320
 
4.3%
ca 16384
 
3.5%
fr 16160
 
3.4%
au 15384
 
3.3%
nl 9280
 
2.0%
br 7508
 
1.6%
jp 6742
 
1.4%
Other values (239) 108290
23.0%
2023-12-28T21:53:46.793967image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (45) 148900
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 939685
> 99.9%
Lowercase Letter 177
 
< 0.1%
Decimal Number 8
 
< 0.1%
Space Separator 3
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (16) 148709
15.8%
Lowercase Letter
ValueCountFrequency (%)
e 24
13.6%
a 19
10.7%
n 17
9.6%
t 15
 
8.5%
l 14
 
7.9%
r 14
 
7.9%
d 11
 
6.2%
i 11
 
6.2%
s 7
 
4.0%
u 7
 
4.0%
Other values (11) 38
21.5%
Decimal Number
ValueCountFrequency (%)
2 2
25.0%
7 2
25.0%
1 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
3
100.0%
Math Symbol
ValueCountFrequency (%)
√ 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 939862
> 99.9%
Common 14
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (37) 148886
15.8%
Common
ValueCountFrequency (%)
3
21.4%
√ 2
14.3%
2 2
14.3%
7 2
14.3%
1 2
14.3%
© 1
 
7.1%
0 1
 
7.1%
8 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 939872
> 99.9%
Math Operators 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 213208
22.7%
S 211762
22.5%
B 56918
 
6.1%
N 54200
 
5.8%
G 52456
 
5.6%
I 50599
 
5.4%
E 45211
 
4.8%
A 44023
 
4.7%
R 32257
 
3.4%
C 30342
 
3.2%
Other values (42) 148896
15.8%
Math Operators
ValueCountFrequency (%)
√ 2
100.0%
None
ValueCountFrequency (%)
© 1
50.0%
õ 1
50.0%

state
Categorical

MISSING 

Distinct50
Distinct (%)< 0.1%
Missing319941
Missing (%)62.8%
Memory size30.2 MiB
CA
41553 
NY
20829 
TX
13310 
FL
10321 
IL
 
7908
Other values (45)
95737 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters379316
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMN
2nd rowMD
3rd rowFL
4th rowNH
5th rowWA

Common Values

ValueCountFrequency (%)
CA 41553
 
8.2%
NY 20829
 
4.1%
TX 13310
 
2.6%
FL 10321
 
2.0%
IL 7908
 
1.6%
MA 7304
 
1.4%
WA 6142
 
1.2%
CO 5923
 
1.2%
GA 5336
 
1.0%
PA 5184
 
1.0%
Other values (40) 65848
 
12.9%
(Missing) 319941
62.8%

Length

2023-12-28T21:53:47.011347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 41553
21.9%
ny 20829
 
11.0%
tx 13310
 
7.0%
fl 10321
 
5.4%
il 7908
 
4.2%
ma 7304
 
3.9%
wa 6142
 
3.2%
co 5923
 
3.1%
ga 5336
 
2.8%
pa 5184
 
2.7%
Other values (40) 65848
34.7%

Most occurring characters

ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 379316
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 379316
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 77434
20.4%
C 55145
14.5%
N 41859
11.0%
Y 22368
 
5.9%
T 21303
 
5.6%
M 20816
 
5.5%
L 20295
 
5.4%
I 18617
 
4.9%
O 16631
 
4.4%
X 13310
 
3.5%
Other values (14) 71538
18.9%

industry_grouped
Categorical

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing69660
Missing (%)13.7%
Memory size35.0 MiB
9. Other
227796 
1. Tech - Computer systems design and related services
59896 
6. Manufacturing
39102 
3. Finance
27997 
5. Retail
23768 
Other values (4)
61380 

Length

Max length54
Median length8
Mean length16.308479
Min length8

Characters and Unicode

Total characters7174736
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9. Other
2nd row9. Other
3rd row9. Other
4th row3. Finance
5th row9. Other

Common Values

ValueCountFrequency (%)
9. Other 227796
44.7%
1. Tech - Computer systems design and related services 59896
 
11.8%
6. Manufacturing 39102
 
7.7%
3. Finance 27997
 
5.5%
5. Retail 23768
 
4.7%
7. Wholesale Trade 20318
 
4.0%
4. Consulting 19317
 
3.8%
8. Healthcare 15149
 
3.0%
2. Tech - Software Publisher 6596
 
1.3%
(Missing) 69660
 
13.7%

Length

2023-12-28T21:53:47.195138image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:47.371664image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
9 227796
17.0%
other 227796
17.0%
tech 66492
 
5.0%
66492
 
5.0%
1 59896
 
4.5%
computer 59896
 
4.5%
systems 59896
 
4.5%
design 59896
 
4.5%
and 59896
 
4.5%
related 59896
 
4.5%
Other values (17) 391304
29.2%

Most occurring characters

ValueCountFrequency (%)
899317
 
12.5%
e 869769
 
12.1%
t 511416
 
7.1%
r 495245
 
6.9%
. 439939
 
6.1%
s 405607
 
5.7%
h 336351
 
4.7%
a 327291
 
4.6%
n 292624
 
4.1%
i 236572
 
3.3%
Other values (33) 2360605
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4795704
66.8%
Space Separator 899317
 
12.5%
Uppercase Letter 533345
 
7.4%
Other Punctuation 439939
 
6.1%
Decimal Number 439939
 
6.1%
Dash Punctuation 66492
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 869769
18.1%
t 511416
10.7%
r 495245
10.3%
s 405607
8.5%
h 336351
 
7.0%
a 327291
 
6.8%
n 292624
 
6.1%
i 236572
 
4.9%
c 208636
 
4.4%
d 200006
 
4.2%
Other values (11) 912187
19.0%
Uppercase Letter
ValueCountFrequency (%)
O 227796
42.7%
T 86810
 
16.3%
C 79213
 
14.9%
M 39102
 
7.3%
F 27997
 
5.2%
R 23768
 
4.5%
W 20318
 
3.8%
H 15149
 
2.8%
S 6596
 
1.2%
P 6596
 
1.2%
Decimal Number
ValueCountFrequency (%)
9 227796
51.8%
1 59896
 
13.6%
6 39102
 
8.9%
3 27997
 
6.4%
5 23768
 
5.4%
7 20318
 
4.6%
4 19317
 
4.4%
8 15149
 
3.4%
2 6596
 
1.5%
Space Separator
ValueCountFrequency (%)
899317
100.0%
Other Punctuation
ValueCountFrequency (%)
. 439939
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5329049
74.3%
Common 1845687
 
25.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 869769
16.3%
t 511416
 
9.6%
r 495245
 
9.3%
s 405607
 
7.6%
h 336351
 
6.3%
a 327291
 
6.1%
n 292624
 
5.5%
i 236572
 
4.4%
O 227796
 
4.3%
c 208636
 
3.9%
Other values (21) 1417742
26.6%
Common
ValueCountFrequency (%)
899317
48.7%
. 439939
23.8%
9 227796
 
12.3%
- 66492
 
3.6%
1 59896
 
3.2%
6 39102
 
2.1%
3 27997
 
1.5%
5 23768
 
1.3%
7 20318
 
1.1%
4 19317
 
1.0%
Other values (2) 21745
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7174736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
899317
 
12.5%
e 869769
 
12.1%
t 511416
 
7.1%
r 495245
 
6.9%
. 439939
 
6.1%
s 405607
 
5.7%
h 336351
 
4.7%
a 327291
 
4.6%
n 292624
 
4.1%
i 236572
 
3.3%
Other values (33) 2360605
32.9%

has_crossbeam_data
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
326935 
True
182664 
ValueCountFrequency (%)
False 326935
64.2%
True 182664
35.8%
2023-12-28T21:53:47.545058image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

crossbeam_product1_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182904 
1.0
 
225

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182904
35.9%
1.0 225
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:47.673819image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:47.797281image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182904
99.9%
1.0 225
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 366033
66.6%
. 183129
33.3%
1 225
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366033
99.9%
1 225
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366033
66.6%
. 183129
33.3%
1 225
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366033
66.6%
. 183129
33.3%
1 225
 
< 0.1%

crossbeam_product2_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182923 
1.0
 
206

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182923
35.9%
1.0 206
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:47.936138image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:48.075610image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182923
99.9%
1.0 206
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 366052
66.6%
. 183129
33.3%
1 206
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366052
99.9%
1 206
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366052
66.6%
. 183129
33.3%
1 206
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366052
66.6%
. 183129
33.3%
1 206
 
< 0.1%

crossbeam_product3_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182840 
1.0
 
289

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182840
35.9%
1.0 289
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:48.251980image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:48.423562image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182840
99.8%
1.0 289
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 365969
66.6%
. 183129
33.3%
1 289
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365969
99.9%
1 289
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365969
66.6%
. 183129
33.3%
1 289
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365969
66.6%
. 183129
33.3%
1 289
 
0.1%

crossbeam_product4_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
183058 
1.0
 
71

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 183058
35.9%
1.0 71
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:48.604953image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:48.760064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 183058
> 99.9%
1.0 71
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 366187
66.7%
. 183129
33.3%
1 71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366187
> 99.9%
1 71
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366187
66.7%
. 183129
33.3%
1 71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366187
66.7%
. 183129
33.3%
1 71
 
< 0.1%

crossbeam_product5_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182311 
1.0
 
818

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182311
35.8%
1.0 818
 
0.2%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:48.916195image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:49.049909image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182311
99.6%
1.0 818
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 365440
66.5%
. 183129
33.3%
1 818
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365440
99.8%
1 818
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365440
66.5%
. 183129
33.3%
1 818
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365440
66.5%
. 183129
33.3%
1 818
 
0.1%

crossbeam_product6_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
175464 
1.0
 
7665

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 175464
34.4%
1.0 7665
 
1.5%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:49.193841image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:49.345621image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 175464
95.8%
1.0 7665
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 358593
65.3%
. 183129
33.3%
1 7665
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 358593
97.9%
1 7665
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 358593
65.3%
. 183129
33.3%
1 7665
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 358593
65.3%
. 183129
33.3%
1 7665
 
1.4%

crossbeam_product7_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182444 
1.0
 
685

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182444
35.8%
1.0 685
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:49.502040image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:49.641527image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182444
99.6%
1.0 685
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 365573
66.5%
. 183129
33.3%
1 685
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365573
99.8%
1 685
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365573
66.5%
. 183129
33.3%
1 685
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365573
66.5%
. 183129
33.3%
1 685
 
0.1%

crossbeam_product8_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182150 
1.0
 
979

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182150
35.7%
1.0 979
 
0.2%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:49.798210image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:49.938212image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182150
99.5%
1.0 979
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 365279
66.5%
. 183129
33.3%
1 979
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365279
99.7%
1 979
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365279
66.5%
. 183129
33.3%
1 979
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365279
66.5%
. 183129
33.3%
1 979
 
0.2%

crossbeam_product9_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182927 
1.0
 
202

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182927
35.9%
1.0 202
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:50.141541image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:50.274515image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182927
99.9%
1.0 202
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 366056
66.6%
. 183129
33.3%
1 202
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366056
99.9%
1 202
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366056
66.6%
. 183129
33.3%
1 202
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366056
66.6%
. 183129
33.3%
1 202
 
< 0.1%

crossbeam_product10_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
181577 
1.0
 
1552

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 181577
35.6%
1.0 1552
 
0.3%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:50.425777image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:50.567782image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 181577
99.2%
1.0 1552
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 364706
66.4%
. 183129
33.3%
1 1552
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 364706
99.6%
1 1552
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 364706
66.4%
. 183129
33.3%
1 1552
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 364706
66.4%
. 183129
33.3%
1 1552
 
0.3%

crossbeam_product11_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182540 
1.0
 
589

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182540
35.8%
1.0 589
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:50.712142image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:50.848772image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182540
99.7%
1.0 589
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 365669
66.6%
. 183129
33.3%
1 589
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365669
99.8%
1 589
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365669
66.6%
. 183129
33.3%
1 589
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365669
66.6%
. 183129
33.3%
1 589
 
0.1%

crossbeam_product12_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182097 
1.0
 
1032

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182097
35.7%
1.0 1032
 
0.2%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:51.006733image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:51.155667image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182097
99.4%
1.0 1032
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 365226
66.5%
. 183129
33.3%
1 1032
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365226
99.7%
1 1032
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365226
66.5%
. 183129
33.3%
1 1032
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365226
66.5%
. 183129
33.3%
1 1032
 
0.2%

crossbeam_product13_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
183007 
1.0
 
122

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 183007
35.9%
1.0 122
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:51.286293image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:51.417425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 183007
99.9%
1.0 122
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 366136
66.6%
. 183129
33.3%
1 122
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366136
> 99.9%
1 122
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366136
66.6%
. 183129
33.3%
1 122
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366136
66.6%
. 183129
33.3%
1 122
 
< 0.1%

crossbeam_product14_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
181861 
1.0
 
1268

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 181861
35.7%
1.0 1268
 
0.2%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:51.555568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:51.692673image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 181861
99.3%
1.0 1268
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 364990
66.4%
. 183129
33.3%
1 1268
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 364990
99.7%
1 1268
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 364990
66.4%
. 183129
33.3%
1 1268
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 364990
66.4%
. 183129
33.3%
1 1268
 
0.2%

crossbeam_product15_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
177973 
1.0
 
5156

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 177973
34.9%
1.0 5156
 
1.0%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:51.841909image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:51.976671image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 177973
97.2%
1.0 5156
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 361102
65.7%
. 183129
33.3%
1 5156
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 361102
98.6%
1 5156
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 361102
65.7%
. 183129
33.3%
1 5156
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 361102
65.7%
. 183129
33.3%
1 5156
 
0.9%

crossbeam_product16_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182849 
1.0
 
280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182849
35.9%
1.0 280
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:52.148717image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:52.287348image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182849
99.8%
1.0 280
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 365978
66.6%
. 183129
33.3%
1 280
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365978
99.9%
1 280
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365978
66.6%
. 183129
33.3%
1 280
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365978
66.6%
. 183129
33.3%
1 280
 
0.1%

crossbeam_product17_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182875 
1.0
 
254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182875
35.9%
1.0 254
 
< 0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:52.421232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:52.558067image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182875
99.9%
1.0 254
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 366004
66.6%
. 183129
33.3%
1 254
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366004
99.9%
1 254
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366004
66.6%
. 183129
33.3%
1 254
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366004
66.6%
. 183129
33.3%
1 254
 
< 0.1%

crossbeam_product18_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182398 
1.0
 
731

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182398
35.8%
1.0 731
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:52.698614image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:52.851879image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182398
99.6%
1.0 731
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 365527
66.5%
. 183129
33.3%
1 731
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365527
99.8%
1 731
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365527
66.5%
. 183129
33.3%
1 731
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365527
66.5%
. 183129
33.3%
1 731
 
0.1%

crossbeam_product19_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
181481 
1.0
 
1648

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 181481
35.6%
1.0 1648
 
0.3%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:53.000143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:53.132900image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 181481
99.1%
1.0 1648
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 364610
66.4%
. 183129
33.3%
1 1648
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 364610
99.6%
1 1648
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 364610
66.4%
. 183129
33.3%
1 1648
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 364610
66.4%
. 183129
33.3%
1 1648
 
0.3%

crossbeam_product20_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
172693 
1.0
 
10436

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 172693
33.9%
1.0 10436
 
2.0%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:53.276278image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:53.440937image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 172693
94.3%
1.0 10436
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 355822
64.8%
. 183129
33.3%
1 10436
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 355822
97.2%
1 10436
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 355822
64.8%
. 183129
33.3%
1 10436
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 355822
64.8%
. 183129
33.3%
1 10436
 
1.9%

crossbeam_product21_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182080 
1.0
 
1049

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182080
35.7%
1.0 1049
 
0.2%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:53.607029image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:53.770052image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182080
99.4%
1.0 1049
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 365209
66.5%
. 183129
33.3%
1 1049
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365209
99.7%
1 1049
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365209
66.5%
. 183129
33.3%
1 1049
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365209
66.5%
. 183129
33.3%
1 1049
 
0.2%

crossbeam_product22_customer
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
182627 
1.0
 
502

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 182627
35.8%
1.0 502
 
0.1%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:53.936655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:54.083709image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 182627
99.7%
1.0 502
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 365756
66.6%
. 183129
33.3%
1 502
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365756
99.9%
1 502
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365756
66.6%
. 183129
33.3%
1 502
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365756
66.6%
. 183129
33.3%
1 502
 
0.1%

crossbeam_product23_customer
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing326470
Missing (%)64.1%
Memory size30.4 MiB
0.0
152515 
1.0
30614 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters549387
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 152515
29.9%
1.0 30614
 
6.0%
(Missing) 326470
64.1%

Length

2023-12-28T21:53:54.237113image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:54.400042image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 152515
83.3%
1.0 30614
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0 335644
61.1%
. 183129
33.3%
1 30614
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 366258
66.7%
Other Punctuation 183129
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 335644
91.6%
1 30614
 
8.4%
Other Punctuation
ValueCountFrequency (%)
. 183129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 549387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 335644
61.1%
. 183129
33.3%
1 30614
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 335644
61.1%
. 183129
33.3%
1 30614
 
5.6%

dnb_founded_time_grouped
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing301502
Missing (%)59.2%
Memory size31.8 MiB
After 2000
138001 
Before 2000
70096 

Length

Max length11
Median length10
Mean length10.336843
Min length10

Characters and Unicode

Total characters2151066
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBefore 2000
2nd rowAfter 2000
3rd rowAfter 2000
4th rowBefore 2000
5th rowBefore 2000

Common Values

ValueCountFrequency (%)
After 2000 138001
27.1%
Before 2000 70096
 
13.8%
(Missing) 301502
59.2%

Length

2023-12-28T21:53:54.565383image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:54.714984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
2000 208097
50.0%
after 138001
33.2%
before 70096
 
16.8%

Most occurring characters

ValueCountFrequency (%)
0 624291
29.0%
e 278193
12.9%
f 208097
 
9.7%
r 208097
 
9.7%
208097
 
9.7%
2 208097
 
9.7%
A 138001
 
6.4%
t 138001
 
6.4%
B 70096
 
3.3%
o 70096
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 902484
42.0%
Decimal Number 832388
38.7%
Space Separator 208097
 
9.7%
Uppercase Letter 208097
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 278193
30.8%
f 208097
23.1%
r 208097
23.1%
t 138001
15.3%
o 70096
 
7.8%
Decimal Number
ValueCountFrequency (%)
0 624291
75.0%
2 208097
 
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 138001
66.3%
B 70096
33.7%
Space Separator
ValueCountFrequency (%)
208097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1110581
51.6%
Common 1040485
48.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 278193
25.0%
f 208097
18.7%
r 208097
18.7%
A 138001
12.4%
t 138001
12.4%
B 70096
 
6.3%
o 70096
 
6.3%
Common
ValueCountFrequency (%)
0 624291
60.0%
208097
 
20.0%
2 208097
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2151066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 624291
29.0%
e 278193
12.9%
f 208097
 
9.7%
r 208097
 
9.7%
208097
 
9.7%
2 208097
 
9.7%
A 138001
 
6.4%
t 138001
 
6.4%
B 70096
 
3.3%
o 70096
 
3.3%

has_hg_data
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size497.8 KiB
False
461636 
True
47963 
ValueCountFrequency (%)
False 461636
90.6%
True 47963
 
9.4%
2023-12-28T21:53:54.843920image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

hg_product_27
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49207 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49207
 
9.7%
1.0 1
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:54.981779image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:55.112985image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49207
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98415
66.7%
. 49208
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98415
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98415
66.7%
. 49208
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98415
66.7%
. 49208
33.3%
1 1
 
< 0.1%

hg_product_28
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45570 
1.0
 
3638

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45570
 
8.9%
1.0 3638
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:55.260650image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:55.405605image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45570
92.6%
1.0 3638
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 94778
64.2%
. 49208
33.3%
1 3638
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94778
96.3%
1 3638
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94778
64.2%
. 49208
33.3%
1 3638
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94778
64.2%
. 49208
33.3%
1 3638
 
2.5%

hg_product_29
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48364 
1.0
 
844

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48364
 
9.5%
1.0 844
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:55.559910image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:55.689437image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48364
98.3%
1.0 844
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 97572
66.1%
. 49208
33.3%
1 844
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97572
99.1%
1 844
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97572
66.1%
. 49208
33.3%
1 844
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97572
66.1%
. 49208
33.3%
1 844
 
0.6%

hg_product_30
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48494 
1.0
 
714

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48494
 
9.5%
1.0 714
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:55.822377image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:55.949806image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48494
98.5%
1.0 714
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 97702
66.2%
. 49208
33.3%
1 714
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97702
99.3%
1 714
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97702
66.2%
. 49208
33.3%
1 714
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97702
66.2%
. 49208
33.3%
1 714
 
0.5%

hg_product_31
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
42277 
1.0
6931 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42277
 
8.3%
1.0 6931
 
1.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:56.083756image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:56.208683image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42277
85.9%
1.0 6931
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 91485
62.0%
. 49208
33.3%
1 6931
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91485
93.0%
1 6931
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91485
62.0%
. 49208
33.3%
1 6931
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91485
62.0%
. 49208
33.3%
1 6931
 
4.7%

hg_product_32
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
31188 
1.0
18020 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 31188
 
6.1%
1.0 18020
 
3.5%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:56.347269image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:56.478091image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 31188
63.4%
1.0 18020
36.6%

Most occurring characters

ValueCountFrequency (%)
0 80396
54.5%
. 49208
33.3%
1 18020
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 80396
81.7%
1 18020
 
18.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 80396
54.5%
. 49208
33.3%
1 18020
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 80396
54.5%
. 49208
33.3%
1 18020
 
12.2%

hg_product_33
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
39219 
1.0
9989 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 39219
 
7.7%
1.0 9989
 
2.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:56.619236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:56.750224image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 39219
79.7%
1.0 9989
 
20.3%

Most occurring characters

ValueCountFrequency (%)
0 88427
59.9%
. 49208
33.3%
1 9989
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88427
89.9%
1 9989
 
10.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88427
59.9%
. 49208
33.3%
1 9989
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88427
59.9%
. 49208
33.3%
1 9989
 
6.8%

hg_product_34
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
38566 
1.0
10642 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38566
 
7.6%
1.0 10642
 
2.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:56.900196image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:57.038874image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38566
78.4%
1.0 10642
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0 87774
59.5%
. 49208
33.3%
1 10642
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87774
89.2%
1 10642
 
10.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87774
59.5%
. 49208
33.3%
1 10642
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87774
59.5%
. 49208
33.3%
1 10642
 
7.2%

hg_product_35
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48862 
1.0
 
346

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48862
 
9.6%
1.0 346
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:57.195258image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:57.346400image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48862
99.3%
1.0 346
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 98070
66.4%
. 49208
33.3%
1 346
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98070
99.6%
1 346
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98070
66.4%
. 49208
33.3%
1 346
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98070
66.4%
. 49208
33.3%
1 346
 
0.2%

hg_product_36
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44725 
1.0
4483 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44725
 
8.8%
1.0 4483
 
0.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:57.485271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:57.614481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44725
90.9%
1.0 4483
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 93933
63.6%
. 49208
33.3%
1 4483
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93933
95.4%
1 4483
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93933
63.6%
. 49208
33.3%
1 4483
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93933
63.6%
. 49208
33.3%
1 4483
 
3.0%

hg_product_37
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46392 
1.0
 
2816

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46392
 
9.1%
1.0 2816
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:57.767314image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:57.914708image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46392
94.3%
1.0 2816
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 95600
64.8%
. 49208
33.3%
1 2816
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95600
97.1%
1 2816
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95600
64.8%
. 49208
33.3%
1 2816
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95600
64.8%
. 49208
33.3%
1 2816
 
1.9%

hg_product_38
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
42891 
1.0
6317 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42891
 
8.4%
1.0 6317
 
1.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:58.060329image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:58.205848image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42891
87.2%
1.0 6317
 
12.8%

Most occurring characters

ValueCountFrequency (%)
0 92099
62.4%
. 49208
33.3%
1 6317
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92099
93.6%
1 6317
 
6.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92099
62.4%
. 49208
33.3%
1 6317
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92099
62.4%
. 49208
33.3%
1 6317
 
4.3%

hg_product_39
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44203 
1.0
5005 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44203
 
8.7%
1.0 5005
 
1.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:58.379998image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:58.546482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44203
89.8%
1.0 5005
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 93411
63.3%
. 49208
33.3%
1 5005
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93411
94.9%
1 5005
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93411
63.3%
. 49208
33.3%
1 5005
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93411
63.3%
. 49208
33.3%
1 5005
 
3.4%

hg_product_40
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49025 
1.0
 
183

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49025
 
9.6%
1.0 183
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:58.746829image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:58.959222image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49025
99.6%
1.0 183
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 98233
66.5%
. 49208
33.3%
1 183
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98233
99.8%
1 183
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98233
66.5%
. 49208
33.3%
1 183
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98233
66.5%
. 49208
33.3%
1 183
 
0.1%

hg_product_41
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45407 
1.0
 
3801

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45407
 
8.9%
1.0 3801
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:59.202038image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:59.409020image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45407
92.3%
1.0 3801
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 94615
64.1%
. 49208
33.3%
1 3801
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94615
96.1%
1 3801
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94615
64.1%
. 49208
33.3%
1 3801
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94615
64.1%
. 49208
33.3%
1 3801
 
2.6%

hg_product_42
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45417 
1.0
 
3791

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45417
 
8.9%
1.0 3791
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:59.587846image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:53:59.733191image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45417
92.3%
1.0 3791
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 94625
64.1%
. 49208
33.3%
1 3791
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94625
96.1%
1 3791
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94625
64.1%
. 49208
33.3%
1 3791
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94625
64.1%
. 49208
33.3%
1 3791
 
2.6%

hg_product_43
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48492 
1.0
 
716

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48492
 
9.5%
1.0 716
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:53:59.880082image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:00.033792image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48492
98.5%
1.0 716
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 97700
66.2%
. 49208
33.3%
1 716
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97700
99.3%
1 716
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97700
66.2%
. 49208
33.3%
1 716
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97700
66.2%
. 49208
33.3%
1 716
 
0.5%

hg_product_44
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44687 
1.0
4521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44687
 
8.8%
1.0 4521
 
0.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:00.225753image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:00.388330image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44687
90.8%
1.0 4521
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 93895
63.6%
. 49208
33.3%
1 4521
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93895
95.4%
1 4521
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93895
63.6%
. 49208
33.3%
1 4521
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93895
63.6%
. 49208
33.3%
1 4521
 
3.1%

hg_product_45
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
43092 
1.0
6116 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 43092
 
8.5%
1.0 6116
 
1.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:00.548254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:00.682818image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43092
87.6%
1.0 6116
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 92300
62.5%
. 49208
33.3%
1 6116
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92300
93.8%
1 6116
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92300
62.5%
. 49208
33.3%
1 6116
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92300
62.5%
. 49208
33.3%
1 6116
 
4.1%

hg_product_46
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49111 
1.0
 
97

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49111
 
9.6%
1.0 97
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:00.829831image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:00.977310image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49111
99.8%
1.0 97
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 98319
66.6%
. 49208
33.3%
1 97
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98319
99.9%
1 97
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98319
66.6%
. 49208
33.3%
1 97
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98319
66.6%
. 49208
33.3%
1 97
 
0.1%

hg_product_47
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
33813 
1.0
15395 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 33813
 
6.6%
1.0 15395
 
3.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:01.175479image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:01.330526image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 33813
68.7%
1.0 15395
31.3%

Most occurring characters

ValueCountFrequency (%)
0 83021
56.2%
. 49208
33.3%
1 15395
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83021
84.4%
1 15395
 
15.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83021
56.2%
. 49208
33.3%
1 15395
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83021
56.2%
. 49208
33.3%
1 15395
 
10.4%

hg_product_48
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
1.0
30357 
0.0
18851 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 30357
 
6.0%
0.0 18851
 
3.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:01.475861image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:01.623182image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 30357
61.7%
0.0 18851
38.3%

Most occurring characters

ValueCountFrequency (%)
0 68059
46.1%
. 49208
33.3%
1 30357
20.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68059
69.2%
1 30357
30.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68059
46.1%
. 49208
33.3%
1 30357
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68059
46.1%
. 49208
33.3%
1 30357
20.6%

hg_product_49
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47500 
1.0
 
1708

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47500
 
9.3%
1.0 1708
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:01.765427image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:01.898339image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47500
96.5%
1.0 1708
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 96708
65.5%
. 49208
33.3%
1 1708
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96708
98.3%
1 1708
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96708
65.5%
. 49208
33.3%
1 1708
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96708
65.5%
. 49208
33.3%
1 1708
 
1.2%

hg_product_50
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
42682 
1.0
6526 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42682
 
8.4%
1.0 6526
 
1.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:02.035928image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:02.163228image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42682
86.7%
1.0 6526
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 91890
62.2%
. 49208
33.3%
1 6526
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91890
93.4%
1 6526
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91890
62.2%
. 49208
33.3%
1 6526
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91890
62.2%
. 49208
33.3%
1 6526
 
4.4%

hg_product_51
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46945 
1.0
 
2263

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46945
 
9.2%
1.0 2263
 
0.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:02.297228image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:02.438382image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46945
95.4%
1.0 2263
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 96153
65.1%
. 49208
33.3%
1 2263
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96153
97.7%
1 2263
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96153
65.1%
. 49208
33.3%
1 2263
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96153
65.1%
. 49208
33.3%
1 2263
 
1.5%

hg_product_52
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47728 
1.0
 
1480

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47728
 
9.4%
1.0 1480
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:02.574551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:02.696729image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47728
97.0%
1.0 1480
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 96936
65.7%
. 49208
33.3%
1 1480
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96936
98.5%
1 1480
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96936
65.7%
. 49208
33.3%
1 1480
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96936
65.7%
. 49208
33.3%
1 1480
 
1.0%

hg_product_53
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48410 
1.0
 
798

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48410
 
9.5%
1.0 798
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:02.842260image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:02.968922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48410
98.4%
1.0 798
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 97618
66.1%
. 49208
33.3%
1 798
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97618
99.2%
1 798
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97618
66.1%
. 49208
33.3%
1 798
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97618
66.1%
. 49208
33.3%
1 798
 
0.5%

hg_product_54
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49163 
1.0
 
45

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49163
 
9.6%
1.0 45
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:03.100454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:03.224922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49163
99.9%
1.0 45
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98371
66.6%
. 49208
33.3%
1 45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98371
> 99.9%
1 45
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98371
66.6%
. 49208
33.3%
1 45
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98371
66.6%
. 49208
33.3%
1 45
 
< 0.1%

hg_product_55
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
39493 
1.0
9715 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 39493
 
7.7%
1.0 9715
 
1.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:03.361896image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:03.493859image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 39493
80.3%
1.0 9715
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 88701
60.1%
. 49208
33.3%
1 9715
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88701
90.1%
1 9715
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88701
60.1%
. 49208
33.3%
1 9715
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88701
60.1%
. 49208
33.3%
1 9715
 
6.6%

hg_product_56
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49135 
1.0
 
73

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49135
 
9.6%
1.0 73
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:03.648214image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:03.780462image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49135
99.9%
1.0 73
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98343
66.6%
. 49208
33.3%
1 73
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98343
99.9%
1 73
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98343
66.6%
. 49208
33.3%
1 73
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98343
66.6%
. 49208
33.3%
1 73
 
< 0.1%

hg_product_57
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45746 
1.0
 
3462

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45746
 
9.0%
1.0 3462
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:03.923491image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:04.062180image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45746
93.0%
1.0 3462
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 94954
64.3%
. 49208
33.3%
1 3462
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94954
96.5%
1 3462
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94954
64.3%
. 49208
33.3%
1 3462
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94954
64.3%
. 49208
33.3%
1 3462
 
2.3%

hg_product_58
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49185 
1.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49185
 
9.7%
1.0 23
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:04.209734image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:04.343716image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49185
> 99.9%
1.0 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98393
66.7%
. 49208
33.3%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98393
> 99.9%
1 23
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98393
66.7%
. 49208
33.3%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98393
66.7%
. 49208
33.3%
1 23
 
< 0.1%

hg_product_59
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49198 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49198
 
9.7%
1.0 10
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:04.490032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:04.628875image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49198
> 99.9%
1.0 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98406
> 99.9%
1 10
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

hg_product_60
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48993 
1.0
 
215

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48993
 
9.6%
1.0 215
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:04.775901image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:04.909017image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48993
99.6%
1.0 215
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 98201
66.5%
. 49208
33.3%
1 215
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98201
99.8%
1 215
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98201
66.5%
. 49208
33.3%
1 215
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98201
66.5%
. 49208
33.3%
1 215
 
0.1%

hg_product_61
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49184 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49184
 
9.7%
1.0 24
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:05.090482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:05.232486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49184
> 99.9%
1.0 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98392
> 99.9%
1 24
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

hg_product_62
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46363 
1.0
 
2845

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46363
 
9.1%
1.0 2845
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:05.370021image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:05.495699image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46363
94.2%
1.0 2845
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 95571
64.7%
. 49208
33.3%
1 2845
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95571
97.1%
1 2845
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95571
64.7%
. 49208
33.3%
1 2845
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95571
64.7%
. 49208
33.3%
1 2845
 
1.9%

hg_product_63
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
42557 
1.0
6651 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42557
 
8.4%
1.0 6651
 
1.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:05.646670image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:05.783046image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42557
86.5%
1.0 6651
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 91765
62.2%
. 49208
33.3%
1 6651
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91765
93.2%
1 6651
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91765
62.2%
. 49208
33.3%
1 6651
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91765
62.2%
. 49208
33.3%
1 6651
 
4.5%

hg_product_64
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49075 
1.0
 
133

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49075
 
9.6%
1.0 133
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:05.937997image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:06.073718image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49075
99.7%
1.0 133
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 98283
66.6%
. 49208
33.3%
1 133
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98283
99.9%
1 133
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98283
66.6%
. 49208
33.3%
1 133
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98283
66.6%
. 49208
33.3%
1 133
 
0.1%

hg_product_65
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46567 
1.0
 
2641

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46567
 
9.1%
1.0 2641
 
0.5%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:06.217753image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:06.345550image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46567
94.6%
1.0 2641
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 95775
64.9%
. 49208
33.3%
1 2641
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95775
97.3%
1 2641
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95775
64.9%
. 49208
33.3%
1 2641
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95775
64.9%
. 49208
33.3%
1 2641
 
1.8%

hg_product_66
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48608 
1.0
 
600

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48608
 
9.5%
1.0 600
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:06.478008image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:06.604237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48608
98.8%
1.0 600
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 97816
66.3%
. 49208
33.3%
1 600
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97816
99.4%
1 600
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97816
66.3%
. 49208
33.3%
1 600
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97816
66.3%
. 49208
33.3%
1 600
 
0.4%

hg_product_67
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
28912 
1.0
20296 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28912
 
5.7%
1.0 20296
 
4.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:06.735381image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:06.879229image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28912
58.8%
1.0 20296
41.2%

Most occurring characters

ValueCountFrequency (%)
0 78120
52.9%
. 49208
33.3%
1 20296
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 78120
79.4%
1 20296
 
20.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 78120
52.9%
. 49208
33.3%
1 20296
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 78120
52.9%
. 49208
33.3%
1 20296
 
13.7%

hg_product_68
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44850 
1.0
 
4358

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44850
 
8.8%
1.0 4358
 
0.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:07.035347image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:07.174007image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44850
91.1%
1.0 4358
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 94058
63.7%
. 49208
33.3%
1 4358
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94058
95.6%
1 4358
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94058
63.7%
. 49208
33.3%
1 4358
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94058
63.7%
. 49208
33.3%
1 4358
 
3.0%

hg_product_69
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
43499 
1.0
5709 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 43499
 
8.5%
1.0 5709
 
1.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:07.327922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:07.472091image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43499
88.4%
1.0 5709
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 92707
62.8%
. 49208
33.3%
1 5709
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92707
94.2%
1 5709
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92707
62.8%
. 49208
33.3%
1 5709
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92707
62.8%
. 49208
33.3%
1 5709
 
3.9%

hg_product_70
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47853 
1.0
 
1355

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47853
 
9.4%
1.0 1355
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:07.609126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:07.745804image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47853
97.2%
1.0 1355
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 97061
65.7%
. 49208
33.3%
1 1355
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97061
98.6%
1 1355
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97061
65.7%
. 49208
33.3%
1 1355
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97061
65.7%
. 49208
33.3%
1 1355
 
0.9%

hg_product_71
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48673 
1.0
 
535

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48673
 
9.6%
1.0 535
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:07.881080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:08.022741image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48673
98.9%
1.0 535
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 97881
66.3%
. 49208
33.3%
1 535
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97881
99.5%
1 535
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97881
66.3%
. 49208
33.3%
1 535
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97881
66.3%
. 49208
33.3%
1 535
 
0.4%

hg_product_72
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46079 
1.0
 
3129

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46079
 
9.0%
1.0 3129
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:08.171080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:08.301232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46079
93.6%
1.0 3129
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 95287
64.5%
. 49208
33.3%
1 3129
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95287
96.8%
1 3129
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95287
64.5%
. 49208
33.3%
1 3129
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95287
64.5%
. 49208
33.3%
1 3129
 
2.1%

hg_product_73
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
24817 
1.0
24391 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 24817
 
4.9%
1.0 24391
 
4.8%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:08.434439image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:08.563962image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 24817
50.4%
1.0 24391
49.6%

Most occurring characters

ValueCountFrequency (%)
0 74025
50.1%
. 49208
33.3%
1 24391
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74025
75.2%
1 24391
 
24.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74025
50.1%
. 49208
33.3%
1 24391
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74025
50.1%
. 49208
33.3%
1 24391
 
16.5%

hg_product_74
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46990 
1.0
 
2218

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46990
 
9.2%
1.0 2218
 
0.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:08.702866image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:08.834032image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46990
95.5%
1.0 2218
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 96198
65.2%
. 49208
33.3%
1 2218
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96198
97.7%
1 2218
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96198
65.2%
. 49208
33.3%
1 2218
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96198
65.2%
. 49208
33.3%
1 2218
 
1.5%

hg_product_75
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45800 
1.0
 
3408

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45800
 
9.0%
1.0 3408
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:08.971533image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:09.102053image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45800
93.1%
1.0 3408
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 95008
64.4%
. 49208
33.3%
1 3408
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95008
96.5%
1 3408
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95008
64.4%
. 49208
33.3%
1 3408
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95008
64.4%
. 49208
33.3%
1 3408
 
2.3%

hg_product_76
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
43871 
1.0
5337 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 43871
 
8.6%
1.0 5337
 
1.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:09.242559image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:09.375800image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43871
89.2%
1.0 5337
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 93079
63.1%
. 49208
33.3%
1 5337
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93079
94.6%
1 5337
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93079
63.1%
. 49208
33.3%
1 5337
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93079
63.1%
. 49208
33.3%
1 5337
 
3.6%

hg_product_77
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
43707 
1.0
5501 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 43707
 
8.6%
1.0 5501
 
1.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:09.513857image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:09.639069image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43707
88.8%
1.0 5501
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 92915
62.9%
. 49208
33.3%
1 5501
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92915
94.4%
1 5501
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92915
62.9%
. 49208
33.3%
1 5501
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92915
62.9%
. 49208
33.3%
1 5501
 
3.7%

hg_product_78
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47428 
1.0
 
1780

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47428
 
9.3%
1.0 1780
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:09.782070image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:09.905913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47428
96.4%
1.0 1780
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 96636
65.5%
. 49208
33.3%
1 1780
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96636
98.2%
1 1780
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96636
65.5%
. 49208
33.3%
1 1780
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96636
65.5%
. 49208
33.3%
1 1780
 
1.2%

hg_product_79
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45593 
1.0
 
3615

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45593
 
8.9%
1.0 3615
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:10.038691image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:10.159883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45593
92.7%
1.0 3615
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 94801
64.2%
. 49208
33.3%
1 3615
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94801
96.3%
1 3615
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94801
64.2%
. 49208
33.3%
1 3615
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94801
64.2%
. 49208
33.3%
1 3615
 
2.4%

hg_product_80
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47598 
1.0
 
1610

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47598
 
9.3%
1.0 1610
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:10.301061image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:10.445212image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47598
96.7%
1.0 1610
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 96806
65.6%
. 49208
33.3%
1 1610
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96806
98.4%
1 1610
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96806
65.6%
. 49208
33.3%
1 1610
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96806
65.6%
. 49208
33.3%
1 1610
 
1.1%

hg_product_81
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46253 
1.0
 
2955

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46253
 
9.1%
1.0 2955
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:10.575271image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:10.706623image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46253
94.0%
1.0 2955
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 95461
64.7%
. 49208
33.3%
1 2955
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95461
97.0%
1 2955
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95461
64.7%
. 49208
33.3%
1 2955
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95461
64.7%
. 49208
33.3%
1 2955
 
2.0%

hg_product_82
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49201 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49201
 
9.7%
1.0 7
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:10.841985image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:10.996744image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49201
> 99.9%
1.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98409
66.7%
. 49208
33.3%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98409
> 99.9%
1 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98409
66.7%
. 49208
33.3%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98409
66.7%
. 49208
33.3%
1 7
 
< 0.1%

hg_product_83
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
36562 
1.0
12646 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36562
 
7.2%
1.0 12646
 
2.5%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:11.174026image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:11.311836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36562
74.3%
1.0 12646
 
25.7%

Most occurring characters

ValueCountFrequency (%)
0 85770
58.1%
. 49208
33.3%
1 12646
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85770
87.2%
1 12646
 
12.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85770
58.1%
. 49208
33.3%
1 12646
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85770
58.1%
. 49208
33.3%
1 12646
 
8.6%

hg_product_84
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48477 
1.0
 
731

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48477
 
9.5%
1.0 731
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:11.449077image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:11.574451image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48477
98.5%
1.0 731
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 97685
66.2%
. 49208
33.3%
1 731
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97685
99.3%
1 731
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97685
66.2%
. 49208
33.3%
1 731
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97685
66.2%
. 49208
33.3%
1 731
 
0.5%

hg_product_85
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49198 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49198
 
9.7%
1.0 10
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:11.710456image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:11.838117image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49198
> 99.9%
1.0 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98406
> 99.9%
1 10
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98406
66.7%
. 49208
33.3%
1 10
 
< 0.1%

hg_product_86
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48538 
1.0
 
670

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48538
 
9.5%
1.0 670
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:11.972593image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:12.105500image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48538
98.6%
1.0 670
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 97746
66.2%
. 49208
33.3%
1 670
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97746
99.3%
1 670
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97746
66.2%
. 49208
33.3%
1 670
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97746
66.2%
. 49208
33.3%
1 670
 
0.5%

hg_product_87
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
30196 
1.0
19012 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 30196
 
5.9%
1.0 19012
 
3.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:12.234330image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:12.354044image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 30196
61.4%
1.0 19012
38.6%

Most occurring characters

ValueCountFrequency (%)
0 79404
53.8%
. 49208
33.3%
1 19012
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79404
80.7%
1 19012
 
19.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79404
53.8%
. 49208
33.3%
1 19012
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79404
53.8%
. 49208
33.3%
1 19012
 
12.9%

hg_product_88
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44690 
1.0
4518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44690
 
8.8%
1.0 4518
 
0.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:12.488633image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:12.611568image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44690
90.8%
1.0 4518
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 93898
63.6%
. 49208
33.3%
1 4518
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93898
95.4%
1 4518
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93898
63.6%
. 49208
33.3%
1 4518
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93898
63.6%
. 49208
33.3%
1 4518
 
3.1%

hg_product_89
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48199 
1.0
 
1009

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48199
 
9.5%
1.0 1009
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:12.743780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:12.872620image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48199
97.9%
1.0 1009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 97407
66.0%
. 49208
33.3%
1 1009
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97407
99.0%
1 1009
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97407
66.0%
. 49208
33.3%
1 1009
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97407
66.0%
. 49208
33.3%
1 1009
 
0.7%

hg_product_90
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47898 
1.0
 
1310

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47898
 
9.4%
1.0 1310
 
0.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:13.009268image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:13.163509image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47898
97.3%
1.0 1310
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 97106
65.8%
. 49208
33.3%
1 1310
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97106
98.7%
1 1310
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97106
65.8%
. 49208
33.3%
1 1310
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97106
65.8%
. 49208
33.3%
1 1310
 
0.9%

hg_product_91
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49193 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49193
 
9.7%
1.0 15
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:13.340446image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:13.481702image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49193
> 99.9%
1.0 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98401
66.7%
. 49208
33.3%
1 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98401
> 99.9%
1 15
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98401
66.7%
. 49208
33.3%
1 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98401
66.7%
. 49208
33.3%
1 15
 
< 0.1%

hg_product_92
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48762 
1.0
 
446

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48762
 
9.6%
1.0 446
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:13.645992image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:13.820080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48762
99.1%
1.0 446
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 97970
66.4%
. 49208
33.3%
1 446
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97970
99.5%
1 446
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97970
66.4%
. 49208
33.3%
1 446
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97970
66.4%
. 49208
33.3%
1 446
 
0.3%

hg_product_93
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49206 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49206
 
9.7%
1.0 2
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:14.035655image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:14.222714image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49206
> 99.9%
1.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98414
66.7%
. 49208
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98414
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98414
66.7%
. 49208
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98414
66.7%
. 49208
33.3%
1 2
 
< 0.1%

hg_product_94
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49118 
1.0
 
90

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49118
 
9.6%
1.0 90
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:14.460192image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:14.638285image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49118
99.8%
1.0 90
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 98326
66.6%
. 49208
33.3%
1 90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98326
99.9%
1 90
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98326
66.6%
. 49208
33.3%
1 90
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98326
66.6%
. 49208
33.3%
1 90
 
0.1%

hg_product_95
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47338 
1.0
 
1870

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47338
 
9.3%
1.0 1870
 
0.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:14.790849image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:14.925946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47338
96.2%
1.0 1870
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 96546
65.4%
. 49208
33.3%
1 1870
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96546
98.1%
1 1870
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96546
65.4%
. 49208
33.3%
1 1870
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96546
65.4%
. 49208
33.3%
1 1870
 
1.3%

hg_product_96
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45617 
1.0
 
3591

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45617
 
9.0%
1.0 3591
 
0.7%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:15.064890image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:15.204351image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45617
92.7%
1.0 3591
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 94825
64.2%
. 49208
33.3%
1 3591
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94825
96.4%
1 3591
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94825
64.2%
. 49208
33.3%
1 3591
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94825
64.2%
. 49208
33.3%
1 3591
 
2.4%

hg_product_97
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48938 
1.0
 
270

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48938
 
9.6%
1.0 270
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:15.363709image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:15.495764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48938
99.5%
1.0 270
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 98146
66.5%
. 49208
33.3%
1 270
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98146
99.7%
1 270
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98146
66.5%
. 49208
33.3%
1 270
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98146
66.5%
. 49208
33.3%
1 270
 
0.2%

hg_product_98
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48708 
1.0
 
500

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48708
 
9.6%
1.0 500
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:15.627748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:15.758239image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48708
99.0%
1.0 500
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 97916
66.3%
. 49208
33.3%
1 500
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97916
99.5%
1 500
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97916
66.3%
. 49208
33.3%
1 500
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97916
66.3%
. 49208
33.3%
1 500
 
0.3%

hg_product_99
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48744 
1.0
 
464

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48744
 
9.6%
1.0 464
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:15.896420image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:16.019787image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48744
99.1%
1.0 464
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 97952
66.4%
. 49208
33.3%
1 464
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97952
99.5%
1 464
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97952
66.4%
. 49208
33.3%
1 464
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97952
66.4%
. 49208
33.3%
1 464
 
0.3%

hg_product_100
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47981 
1.0
 
1227

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47981
 
9.4%
1.0 1227
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:16.149730image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:16.272322image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47981
97.5%
1.0 1227
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 97189
65.8%
. 49208
33.3%
1 1227
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97189
98.8%
1 1227
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97189
65.8%
. 49208
33.3%
1 1227
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97189
65.8%
. 49208
33.3%
1 1227
 
0.8%

hg_product_101
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48294 
1.0
 
914

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48294
 
9.5%
1.0 914
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:16.405264image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:16.528264image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48294
98.1%
1.0 914
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 97502
66.0%
. 49208
33.3%
1 914
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97502
99.1%
1 914
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97502
66.0%
. 49208
33.3%
1 914
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97502
66.0%
. 49208
33.3%
1 914
 
0.6%

hg_product_102
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
36680 
1.0
12528 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36680
 
7.2%
1.0 12528
 
2.5%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:16.654278image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:16.781389image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36680
74.5%
1.0 12528
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 85888
58.2%
. 49208
33.3%
1 12528
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85888
87.3%
1 12528
 
12.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85888
58.2%
. 49208
33.3%
1 12528
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85888
58.2%
. 49208
33.3%
1 12528
 
8.5%

hg_product_103
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44973 
1.0
 
4235

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44973
 
8.8%
1.0 4235
 
0.8%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:16.932279image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:17.075188image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44973
91.4%
1.0 4235
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 94181
63.8%
. 49208
33.3%
1 4235
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94181
95.7%
1 4235
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94181
63.8%
. 49208
33.3%
1 4235
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94181
63.8%
. 49208
33.3%
1 4235
 
2.9%

hg_product_104
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48596 
1.0
 
612

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48596
 
9.5%
1.0 612
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:17.211201image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:17.334119image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48596
98.8%
1.0 612
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 97804
66.3%
. 49208
33.3%
1 612
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97804
99.4%
1 612
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97804
66.3%
. 49208
33.3%
1 612
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97804
66.3%
. 49208
33.3%
1 612
 
0.4%

hg_product_105
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46351 
1.0
 
2857

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46351
 
9.1%
1.0 2857
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:17.465607image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:17.587339image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46351
94.2%
1.0 2857
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 95559
64.7%
. 49208
33.3%
1 2857
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95559
97.1%
1 2857
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95559
64.7%
. 49208
33.3%
1 2857
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95559
64.7%
. 49208
33.3%
1 2857
 
1.9%

hg_product_106
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49013 
1.0
 
195

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49013
 
9.6%
1.0 195
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:17.714336image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:17.847039image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49013
99.6%
1.0 195
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98221
99.8%
1 195
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

hg_product_107
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48955 
1.0
 
253

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48955
 
9.6%
1.0 253
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:17.987926image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:18.114696image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48955
99.5%
1.0 253
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 98163
66.5%
. 49208
33.3%
1 253
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98163
99.7%
1 253
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98163
66.5%
. 49208
33.3%
1 253
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98163
66.5%
. 49208
33.3%
1 253
 
0.2%

hg_product_108
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49204 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49204
 
9.7%
1.0 4
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:18.256828image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:18.376342image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49204
> 99.9%
1.0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98412
66.7%
. 49208
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98412
> 99.9%
1 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98412
66.7%
. 49208
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98412
66.7%
. 49208
33.3%
1 4
 
< 0.1%

hg_product_109
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49157 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49157
 
9.6%
1.0 51
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:18.506339image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:18.626433image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49157
99.9%
1.0 51
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98365
66.6%
. 49208
33.3%
1 51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98365
99.9%
1 51
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98365
66.6%
. 49208
33.3%
1 51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98365
66.6%
. 49208
33.3%
1 51
 
< 0.1%

hg_product_110
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49070 
1.0
 
138

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49070
 
9.6%
1.0 138
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:18.768784image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:18.913343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49070
99.7%
1.0 138
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 98278
66.6%
. 49208
33.3%
1 138
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98278
99.9%
1 138
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98278
66.6%
. 49208
33.3%
1 138
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98278
66.6%
. 49208
33.3%
1 138
 
0.1%

hg_product_111
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49186 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49186
 
9.7%
1.0 22
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:19.080775image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:19.223912image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49186
> 99.9%
1.0 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98394
66.7%
. 49208
33.3%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98394
> 99.9%
1 22
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98394
66.7%
. 49208
33.3%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98394
66.7%
. 49208
33.3%
1 22
 
< 0.1%

hg_product_112
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47036 
1.0
 
2172

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47036
 
9.2%
1.0 2172
 
0.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:19.362288image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:19.493043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47036
95.6%
1.0 2172
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 96244
65.2%
. 49208
33.3%
1 2172
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96244
97.8%
1 2172
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96244
65.2%
. 49208
33.3%
1 2172
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96244
65.2%
. 49208
33.3%
1 2172
 
1.5%

hg_product_113
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49192 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49192
 
9.7%
1.0 16
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:19.648582image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:19.784626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49192
> 99.9%
1.0 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98400
66.7%
. 49208
33.3%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98400
> 99.9%
1 16
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98400
66.7%
. 49208
33.3%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98400
66.7%
. 49208
33.3%
1 16
 
< 0.1%

hg_product_114
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
47220 
1.0
 
1988

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 47220
 
9.3%
1.0 1988
 
0.4%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:19.916754image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:20.034675image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 47220
96.0%
1.0 1988
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 96428
65.3%
. 49208
33.3%
1 1988
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96428
98.0%
1 1988
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96428
65.3%
. 49208
33.3%
1 1988
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96428
65.3%
. 49208
33.3%
1 1988
 
1.3%

hg_product_115
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49133 
1.0
 
75

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49133
 
9.6%
1.0 75
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:20.162149image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:20.277262image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49133
99.8%
1.0 75
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98341
99.9%
1 75
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

hg_product_116
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48737 
1.0
 
471

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48737
 
9.6%
1.0 471
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:20.399646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:20.516225image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48737
99.0%
1.0 471
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 97945
66.3%
. 49208
33.3%
1 471
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97945
99.5%
1 471
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97945
66.3%
. 49208
33.3%
1 471
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97945
66.3%
. 49208
33.3%
1 471
 
0.3%

hg_product_117
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49061 
1.0
 
147

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49061
 
9.6%
1.0 147
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:20.638209image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:20.752157image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49061
99.7%
1.0 147
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 98269
66.6%
. 49208
33.3%
1 147
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98269
99.9%
1 147
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98269
66.6%
. 49208
33.3%
1 147
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98269
66.6%
. 49208
33.3%
1 147
 
0.1%

hg_product_118
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49172 
1.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49172
 
9.6%
1.0 36
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:20.874254image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:20.993244image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49172
99.9%
1.0 36
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98380
66.6%
. 49208
33.3%
1 36
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98380
> 99.9%
1 36
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98380
66.6%
. 49208
33.3%
1 36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98380
66.6%
. 49208
33.3%
1 36
 
< 0.1%

hg_product_119
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
46197 
1.0
 
3011

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 46197
 
9.1%
1.0 3011
 
0.6%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:21.118398image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:21.233690image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 46197
93.9%
1.0 3011
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 95405
64.6%
. 49208
33.3%
1 3011
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95405
96.9%
1 3011
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 95405
64.6%
. 49208
33.3%
1 3011
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 95405
64.6%
. 49208
33.3%
1 3011
 
2.0%

hg_product_120
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48767 
1.0
 
441

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48767
 
9.6%
1.0 441
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:21.355898image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:21.469533image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48767
99.1%
1.0 441
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 97975
66.4%
. 49208
33.3%
1 441
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97975
99.6%
1 441
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97975
66.4%
. 49208
33.3%
1 441
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97975
66.4%
. 49208
33.3%
1 441
 
0.3%

hg_product_121
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49133 
1.0
 
75

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49133
 
9.6%
1.0 75
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:21.588426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:21.700977image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49133
99.8%
1.0 75
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98341
99.9%
1 75
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98341
66.6%
. 49208
33.3%
1 75
 
0.1%

hg_product_122
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49199 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49199
 
9.7%
1.0 9
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:21.830349image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:21.948760image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49199
> 99.9%
1.0 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98407
66.7%
. 49208
33.3%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98407
> 99.9%
1 9
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98407
66.7%
. 49208
33.3%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98407
66.7%
. 49208
33.3%
1 9
 
< 0.1%

hg_product_123
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48211 
1.0
 
997

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48211
 
9.5%
1.0 997
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:22.074794image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:22.191682image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48211
98.0%
1.0 997
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 97419
66.0%
. 49208
33.3%
1 997
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97419
99.0%
1 997
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97419
66.0%
. 49208
33.3%
1 997
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97419
66.0%
. 49208
33.3%
1 997
 
0.7%

hg_product_124
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
37948 
1.0
11260 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 37948
 
7.4%
1.0 11260
 
2.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:22.324169image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:22.449578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37948
77.1%
1.0 11260
 
22.9%

Most occurring characters

ValueCountFrequency (%)
0 87156
59.0%
. 49208
33.3%
1 11260
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87156
88.6%
1 11260
 
11.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87156
59.0%
. 49208
33.3%
1 11260
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87156
59.0%
. 49208
33.3%
1 11260
 
7.6%

hg_product_125
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
34094 
1.0
15114 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34094
 
6.7%
1.0 15114
 
3.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:22.610060image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:22.734051image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34094
69.3%
1.0 15114
30.7%

Most occurring characters

ValueCountFrequency (%)
0 83302
56.4%
. 49208
33.3%
1 15114
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83302
84.6%
1 15114
 
15.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83302
56.4%
. 49208
33.3%
1 15114
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83302
56.4%
. 49208
33.3%
1 15114
 
10.2%

hg_product_126
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48553 
1.0
 
655

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48553
 
9.5%
1.0 655
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:22.870987image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:22.988957image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48553
98.7%
1.0 655
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 97761
66.2%
. 49208
33.3%
1 655
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97761
99.3%
1 655
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97761
66.2%
. 49208
33.3%
1 655
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97761
66.2%
. 49208
33.3%
1 655
 
0.4%

hg_product_127
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48693 
1.0
 
515

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48693
 
9.6%
1.0 515
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:23.120001image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:23.256101image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48693
99.0%
1.0 515
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 97901
66.3%
. 49208
33.3%
1 515
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97901
99.5%
1 515
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97901
66.3%
. 49208
33.3%
1 515
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97901
66.3%
. 49208
33.3%
1 515
 
0.3%

hg_product_128
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49179 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49179
 
9.7%
1.0 29
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:23.396991image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:23.525130image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49179
99.9%
1.0 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98387
66.6%
. 49208
33.3%
1 29
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98387
> 99.9%
1 29
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98387
66.6%
. 49208
33.3%
1 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98387
66.6%
. 49208
33.3%
1 29
 
< 0.1%

hg_product_129
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
28622 
1.0
20586 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28622
 
5.6%
1.0 20586
 
4.0%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:23.665889image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:23.807944image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28622
58.2%
1.0 20586
41.8%

Most occurring characters

ValueCountFrequency (%)
0 77830
52.7%
. 49208
33.3%
1 20586
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77830
79.1%
1 20586
 
20.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77830
52.7%
. 49208
33.3%
1 20586
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77830
52.7%
. 49208
33.3%
1 20586
 
13.9%

hg_product_130
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49060 
1.0
 
148

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49060
 
9.6%
1.0 148
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:23.950177image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:24.075994image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49060
99.7%
1.0 148
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 98268
66.6%
. 49208
33.3%
1 148
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98268
99.8%
1 148
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98268
66.6%
. 49208
33.3%
1 148
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98268
66.6%
. 49208
33.3%
1 148
 
0.1%

hg_product_131
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48977 
1.0
 
231

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48977
 
9.6%
1.0 231
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:24.208622image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:24.354486image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48977
99.5%
1.0 231
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 98185
66.5%
. 49208
33.3%
1 231
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98185
99.8%
1 231
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98185
66.5%
. 49208
33.3%
1 231
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98185
66.5%
. 49208
33.3%
1 231
 
0.2%

hg_product_132
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
43839 
1.0
5369 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 43839
 
8.6%
1.0 5369
 
1.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:24.496138image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:24.620489image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 43839
89.1%
1.0 5369
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 93047
63.0%
. 49208
33.3%
1 5369
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93047
94.5%
1 5369
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93047
63.0%
. 49208
33.3%
1 5369
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93047
63.0%
. 49208
33.3%
1 5369
 
3.6%

hg_product_133
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49013 
1.0
 
195

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49013
 
9.6%
1.0 195
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:24.767327image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:24.909882image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49013
99.6%
1.0 195
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98221
99.8%
1 195
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98221
66.5%
. 49208
33.3%
1 195
 
0.1%

hg_product_134
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49079 
1.0
 
129

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49079
 
9.6%
1.0 129
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:25.053724image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:25.186491image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49079
99.7%
1.0 129
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 98287
66.6%
. 49208
33.3%
1 129
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98287
99.9%
1 129
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98287
66.6%
. 49208
33.3%
1 129
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98287
66.6%
. 49208
33.3%
1 129
 
0.1%

hg_product_135
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49195 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49195
 
9.7%
1.0 13
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:25.333099image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:25.492742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49195
> 99.9%
1.0 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98403
66.7%
. 49208
33.3%
1 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98403
> 99.9%
1 13
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98403
66.7%
. 49208
33.3%
1 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98403
66.7%
. 49208
33.3%
1 13
 
< 0.1%

hg_product_136
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49184 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49184
 
9.7%
1.0 24
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:25.646761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:25.784986image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49184
> 99.9%
1.0 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98392
> 99.9%
1 24
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98392
66.7%
. 49208
33.3%
1 24
 
< 0.1%

hg_product_137
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48735 
1.0
 
473

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48735
 
9.6%
1.0 473
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:25.944498image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:26.082419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48735
99.0%
1.0 473
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 97943
66.3%
. 49208
33.3%
1 473
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97943
99.5%
1 473
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97943
66.3%
. 49208
33.3%
1 473
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97943
66.3%
. 49208
33.3%
1 473
 
0.3%

hg_product_138
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48956 
1.0
 
252

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48956
 
9.6%
1.0 252
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:26.214720image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:26.333670image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48956
99.5%
1.0 252
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 98164
66.5%
. 49208
33.3%
1 252
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98164
99.7%
1 252
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98164
66.5%
. 49208
33.3%
1 252
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98164
66.5%
. 49208
33.3%
1 252
 
0.2%

hg_product_139
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
45354 
1.0
 
3854

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 45354
 
8.9%
1.0 3854
 
0.8%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:26.460454image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:26.579265image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 45354
92.2%
1.0 3854
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0 94562
64.1%
. 49208
33.3%
1 3854
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94562
96.1%
1 3854
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94562
64.1%
. 49208
33.3%
1 3854
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94562
64.1%
. 49208
33.3%
1 3854
 
2.6%

hg_product_140
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
41590 
1.0
7618 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 41590
 
8.2%
1.0 7618
 
1.5%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:26.719803image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:26.858030image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 41590
84.5%
1.0 7618
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 90798
61.5%
. 49208
33.3%
1 7618
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90798
92.3%
1 7618
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90798
61.5%
. 49208
33.3%
1 7618
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90798
61.5%
. 49208
33.3%
1 7618
 
5.2%

hg_product_141
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48263 
1.0
 
945

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48263
 
9.5%
1.0 945
 
0.2%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:26.993836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:27.117050image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48263
98.1%
1.0 945
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 97471
66.0%
. 49208
33.3%
1 945
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97471
99.0%
1 945
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97471
66.0%
. 49208
33.3%
1 945
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97471
66.0%
. 49208
33.3%
1 945
 
0.6%

hg_product_142
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48721 
1.0
 
487

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48721
 
9.6%
1.0 487
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:27.247490image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:27.367578image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48721
99.0%
1.0 487
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 97929
66.3%
. 49208
33.3%
1 487
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97929
99.5%
1 487
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97929
66.3%
. 49208
33.3%
1 487
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97929
66.3%
. 49208
33.3%
1 487
 
0.3%

hg_product_143
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
44799 
1.0
 
4409

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44799
 
8.8%
1.0 4409
 
0.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:27.504977image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:27.645796image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44799
91.0%
1.0 4409
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 94007
63.7%
. 49208
33.3%
1 4409
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94007
95.5%
1 4409
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94007
63.7%
. 49208
33.3%
1 4409
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94007
63.7%
. 49208
33.3%
1 4409
 
3.0%

hg_product_144
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
34538 
1.0
14670 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34538
 
6.8%
1.0 14670
 
2.9%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:27.809768image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:27.937380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34538
70.2%
1.0 14670
29.8%

Most occurring characters

ValueCountFrequency (%)
0 83746
56.7%
. 49208
33.3%
1 14670
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83746
85.1%
1 14670
 
14.9%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83746
56.7%
. 49208
33.3%
1 14670
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83746
56.7%
. 49208
33.3%
1 14670
 
9.9%

hg_product_145
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
42547 
1.0
6661 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42547
 
8.3%
1.0 6661
 
1.3%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:28.082748image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:28.216251image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42547
86.5%
1.0 6661
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 91755
62.2%
. 49208
33.3%
1 6661
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91755
93.2%
1 6661
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91755
62.2%
. 49208
33.3%
1 6661
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91755
62.2%
. 49208
33.3%
1 6661
 
4.5%

hg_product_146
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48994 
1.0
 
214

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48994
 
9.6%
1.0 214
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:28.364854image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:28.499043image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48994
99.6%
1.0 214
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 98202
66.5%
. 49208
33.3%
1 214
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98202
99.8%
1 214
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98202
66.5%
. 49208
33.3%
1 214
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98202
66.5%
. 49208
33.3%
1 214
 
0.1%

hg_product_147
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
48832 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48832
 
9.6%
1.0 376
 
0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:28.654761image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:28.790642image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48832
99.2%
1.0 376
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 98040
66.4%
. 49208
33.3%
1 376
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98040
99.6%
1 376
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98040
66.4%
. 49208
33.3%
1 376
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98040
66.4%
. 49208
33.3%
1 376
 
0.3%

hg_product_148
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing460391
Missing (%)90.3%
Memory size30.9 MiB
0.0
49159 
1.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters147624
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 49159
 
9.6%
1.0 49
 
< 0.1%
(Missing) 460391
90.3%

Length

2023-12-28T21:54:28.919660image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:54:29.037552image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 49159
99.9%
1.0 49
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 98367
66.6%
. 49208
33.3%
1 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98416
66.7%
Other Punctuation 49208
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98367
> 99.9%
1 49
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 49208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98367
66.6%
. 49208
33.3%
1 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98367
66.6%
. 49208
33.3%
1 49
 
< 0.1%

Missing values

2023-12-28T21:52:39.175145image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-28T21:52:45.137778image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-28T21:53:32.794100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

account_idis_current_customeris_self_serviceis_arr_over_12kcompany_revenue_bucketcountrystateindustry_groupedhas_crossbeam_datacrossbeam_product1_customercrossbeam_product2_customercrossbeam_product3_customercrossbeam_product4_customercrossbeam_product5_customercrossbeam_product6_customercrossbeam_product7_customercrossbeam_product8_customercrossbeam_product9_customercrossbeam_product10_customercrossbeam_product11_customercrossbeam_product12_customercrossbeam_product13_customercrossbeam_product14_customercrossbeam_product15_customercrossbeam_product16_customercrossbeam_product17_customercrossbeam_product18_customercrossbeam_product19_customercrossbeam_product20_customercrossbeam_product21_customercrossbeam_product22_customercrossbeam_product23_customerdnb_founded_time_groupedhas_hg_datahg_product_27hg_product_28hg_product_29hg_product_30hg_product_31hg_product_32hg_product_33hg_product_34hg_product_35hg_product_36hg_product_37hg_product_38hg_product_39hg_product_40hg_product_41hg_product_42hg_product_43hg_product_44hg_product_45hg_product_46hg_product_47hg_product_48hg_product_49hg_product_50hg_product_51hg_product_52hg_product_53hg_product_54hg_product_55hg_product_56hg_product_57hg_product_58hg_product_59hg_product_60hg_product_61hg_product_62hg_product_63hg_product_64hg_product_65hg_product_66hg_product_67hg_product_68hg_product_69hg_product_70hg_product_71hg_product_72hg_product_73hg_product_74hg_product_75hg_product_76hg_product_77hg_product_78hg_product_79hg_product_80hg_product_81hg_product_82hg_product_83hg_product_84hg_product_85hg_product_86hg_product_87hg_product_88hg_product_89hg_product_90hg_product_91hg_product_92hg_product_93hg_product_94hg_product_95hg_product_96hg_product_97hg_product_98hg_product_99hg_product_100hg_product_101hg_product_102hg_product_103hg_product_104hg_product_105hg_product_106hg_product_107hg_product_108hg_product_109hg_product_110hg_product_111hg_product_112hg_product_113hg_product_114hg_product_115hg_product_116hg_product_117hg_product_118hg_product_119hg_product_120hg_product_121hg_product_122hg_product_123hg_product_124hg_product_125hg_product_126hg_product_127hg_product_128hg_product_129hg_product_130hg_product_131hg_product_132hg_product_133hg_product_134hg_product_135hg_product_136hg_product_137hg_product_138hg_product_139hg_product_140hg_product_141hg_product_142hg_product_143hg_product_144hg_product_145hg_product_146hg_product_147hg_product_148
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20011G00000qFM5EQAWFalseNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
30011G00000tKtSbQAKFalseNaNNaNNaNRONaN9. OtherFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAfter 2000FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40011G00000tLivvQACFalseNaNNaNNaNINNaN3. FinanceFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNBefore 2000FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
50011G00000uoGZKQA2FalseNaNNaNNaNUSNaN9. OtherFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
60011G00000ufx52QAAFalseNaNNaNNaNMXNaN9. OtherFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNBefore 2000FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
70011G00000xA4EKQA0FalseNaNNaNNaNBRNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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90011G0000135y3dQAAFalseNaNNaNNaNUSMN9. OtherFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5095890011G00000gXzeFQASFalseNaNNaNUnder 100MUSNY3. FinanceTrue0.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5095900011G00000hC5jNQASFalseNaNNaNUnder 1BDENaN4. ConsultingTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000True0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5095910011G00000gZTuJQAWFalseNaNNaNUnder 10BTRNaN6. ManufacturingTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.0NaNTrue0.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
5095920011G00000gY0h8QACFalseNaNNaNUnder 1BUSUT3. FinanceTrue0.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0NaNTrue0.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.0
509593001Ho000014lVGpIAMFalseNaNNaNNaNCHNaN3. FinanceTrue0.00.00.00.01.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.0After 2000True0.01.00.00.01.01.01.01.00.01.00.01.01.00.01.01.00.01.01.00.01.01.00.00.00.00.00.00.01.00.01.00.00.00.00.01.01.00.01.00.01.01.01.01.01.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.00.01.01.01.01.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.01.01.00.00.00.01.00.00.01.00.00.00.00.01.00.01.01.00.00.00.01.01.00.00.00.0
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5095950011G00000gY2yjQACFalseNaNNaNUnder 1BUSWI8. HealthcareTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Before 2000True0.01.00.00.01.01.01.01.00.00.00.01.00.00.00.00.00.00.00.00.01.01.00.01.01.00.00.00.01.00.00.00.00.00.00.00.01.00.01.00.01.01.01.00.00.00.01.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.01.01.00.00.00.00.01.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.01.01.00.00.00.01.01.00.00.00.0
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5095970011G00000hC4rZQASFalseNaNNaNUnder 10BNLNaN3. FinanceTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000True0.00.00.00.00.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.0
5095980011G00000w8kSyQAIFalseNaNNaNUnder 100MGBNaN9. OtherTrue0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0After 2000True0.00.00.00.01.01.00.00.00.01.00.00.00.00.00.00.00.00.01.00.01.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.0